MRIxFields2026-Baseline

mrixfields/MRIxFields2026-Baseline

MRIxFields2026-Baseline is a image-to-image model from mrixfields. Use it when you need one image transformed into another. It is set up for pytorch. The card lists the license as mit.

Pre-trained baseline checkpoints for the MRIxFields 2026 challenge (MICCAI 2026): cross-field brain MRI translation.

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Access

Public

Updated May 9, 2026

Repo size

7.2 GB

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Public

Hugging Face

Repo makeup

Click a slice to open those files.

.pth7.2 GB · 100%

At a glance

Task: Image-to-Image
Library: pytorch
License: mit
Access: Public
Created: May 9, 2026
Updated: May 9, 2026
SHA: f1a7d6b7

Model card

From the Hugging Face model README

MRIxFields2026 Baseline Checkpoints

Pre-trained baseline checkpoints for the MRIxFields 2026 challenge (MICCAI 2026): cross-field brain MRI translation.

Tasks

Task	Goal	Input	Output
Task 1	Ultra-high field synthesis	0.1T / 1.5T / 3T / 5T	7T
Task 2	Low-field enhancement	0.1T	1.5T / 3T / 5T / 7T
Task 3	Any-to-any translation (single unified model)	Any field	Any field

Methods

Method	Paper	Tasks	# sub-tasks
CUT	Park et al., ECCV 2020	1, 2	24
CycleGAN	Zhu et al., ICCV 2017	1, 2	24
StarGAN v2	Choi et al., CVPR 2020	3	1

Total: 49 checkpoints (one per sub-task × method), ~7 GB.

Training

All checkpoints are from the recommended pro_pretrained setting: unpaired pretrain on the retrospective split + paired fine-tune on the prospective split. This is the strongest baseline; ablation modes (pro_scratch, retro_scratch) are kept for internal use and are not released.

Directory Layout

.
├── README.md
├── LICENSE
├── task1_<src>_to_7T_<modality>/
│   ├── cut/pro_pretrained/weights/checkpoint_epoch100.pth
│   └── cyclegan/pro_pretrained/weights/checkpoint_epoch100.pth
├── task2_0.1T_to_<tgt>_<modality>/
│   └── (same as task1)
└── task3_any_to_any_multimodal/
    └── stargan_v2/pro_pretrained/weights/checkpoint_epoch50.pth

Training-time configs (config_actual.yaml) are intentionally not included. Use the config templates in the upstream Baseline repo and override the data paths for your own environment.

Download

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="mrixfields/MRIxFields2026-Baseline",
    repo_type="model",
    local_dir="./MRIxFields2026-Baseline",
)

Single checkpoint:

from huggingface_hub import hf_hub_download

hf_hub_download(
    repo_id="mrixfields/MRIxFields2026-Baseline",
    filename="task1_0.1T_to_7T_T1W/cut/pro_pretrained/weights/checkpoint_epoch100.pth",
    repo_type="model",
)

Training & Inference

See the upstream Baseline code: MRIxFields2026/Baseline.

License

Citation

If you use these baselines, please cite the original method papers:

@inproceedings{park2020cut,
  title={Contrastive Learning for Unpaired Image-to-Image Translation},
  author={Taesung Park and Alexei A. Efros and Richard Zhang and Jun-Yan Zhu},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2020}
}

@inproceedings{CycleGAN2017,
  title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle={IEEE International Conference on Computer Vision (ICCV)},
  year={2017}
}

@inproceedings{choi2020starganv2,
  title={StarGAN v2: Diverse Image Synthesis for Multiple Domains},
  author={Yunjey Choi and Youngjung Uh and Jaejun Yoo and Jung-Woo Ha},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}